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At least 343 records · Page 19Linked to original sources

PubMed2005

A nutrigenomics database--integrated repository for publications and associated microarray data in nutrigenomics research.

In the current situation where microarray data in the field of nutritional genomics (nutrigenomics) are accumulating rapidly, there is imminent need for an efficient data infrastructure to support research workflow. We have established a web-based, integrated database of the publications and microarray expression data in the field of nutrigenomics. The registered data include links to external databases such as PubMed of the National Center for Biotechnology Information and public microarray databases that contain Minimum Information About a Microarray Experiment-compliant microarray expression data. Using this database, all data sets created will be effectively utilized and shared with other researchers. This database is built on an open-source database system and is freely accessible via the World Wide Web (http://a-yo5.ch.a.u-tokyo.ac.jp/index.phtml).

Databases, Genetic↗

PubMed2006

Michigan Molecular Interactions (MiMI): putting the jigsaw puzzle together.

Protein interaction data exists in a number of repositories. Each repository has its own data format, molecule identifier and supplementary information. Michigan Molecular Interactions (MiMI) assists scientists searching through this overwhelming amount of protein interaction data. MiMI gathers data from well-known protein interaction databases and deep-merges the information. Utilizing an identity function, molecules that may have different identifiers but represent the same real-world object are merged. Thus, MiMI allows the users to retrieve information from many different databases at once, highlighting complementary and contradictory information. To help scientists judge the usefulness of a piece of data, MiMI tracks the provenance of all data. Finally, a simple yet powerful user interface aids users in their queries, and frees them from the onerous task of knowing the data format or learning a query language. MiMI allows scientists to query all data, whether corroborative or contradictory, and specify which sources to utilize. MiMI is part of the National Center for Integrative Biomedical Informatics (NCIBI) and is publicly available at: http://mimi.ncibi.org.

Databases, Protein↗

PubMed2002

Ontology development for a pharmacogenetics knowledge base.

Research directed toward discovering how genetic factors influence a patient's response to drugs requires coordination of data produced from laboratory experiments, computational methods, and clinical studies. A public repository of pharmacogenetic data should accelerate progress in the field of pharmacogenetics by organizing and disseminating public datasets. We are developing a pharmacogenetics knowledge base (PharmGKB) to support the storage and retrieval of both experimental data and conceptual knowledge. PharmGKB is an Internet-based resource that integrates complex biological, pharmacological, and clinical data in such a way that researchers can submit their data and users can retrieve information to investigate genotype-phenotype correlations. Successful management of the names, meaning, and organization of concepts used within the system is crucial. We have selected a frame-based knowledge-representation system for development of an ontology of concepts and relationships that represent the domain and that permit storage of experimental data. Preliminary experience shows that the ontology we have developed for gene-sequence data allows us to accept, store, and query data submissions.

Antineoplastic Agents↗

PubMed1991

The integrated hospital database in an open architecture environment.

Although open architecture has a number of advantages, it establishes a need to integrate information currently residing on numerous separate systems. There is no easy solution to retrieve all the data from a single application. An integrated hospital database can serve as a repository for key data from all the various systems.

Computer Systems↗

PubMed1994

The GDB Human Genome Data Base anno 1994.

In 1991 the Genome Data Base at Johns Hopkins University School of Medicine was selected as the central repository for mapping data from the Human Genome Project, and was funded by NIH and DOE under a three year award. GDB has now finished 28 months of Federally funded operation. During this period a great deal of progress and many internal changes have taken place. In addition, many changes have also occurred in the external environment, and GDB has adapted its strategies to play an appropriate role in those changes as well. Recognizing the central role of mapping information in the genome project, it is important that GDB respond aggressively to the increasing demands of genomic researchers, as well as formulate a program of response to a number of long standing, but still unmet, needs of that community. It is even more important that GDB provide leadership in the genome informatics enterprise. Three themes described here are dominant in our future plans and represent the essence of the major changes made in the past year. They include: enhanced data acquisition, better map representation, and full integration into the collection of genomic databases.

Computer Communication Networks↗

PubMed2001

The Functional Magnetic Resonance Imaging Data Center (fMRIDC): the challenges and rewards of large-scale databasing of neuroimaging studies.

The Functional Magnetic Resonance Imaging Data Center (fMRIDC) (http://www.fmridc.org) was established in the Autumn of 1999 with the objective of creating a mechanism by which members of the neuroscientific community may more easily share functional neuroimaging data. Examples in other sciences offer proof of the usefulness and benefit that sharing data provides through encouraging growth and development in those fields. By building a publicly accessible repository of raw data from peer-reviewed studies, the Data Center hopes to create a similarly successful environment for the neurosciences. In this article, we discuss the continuum of data-sharing efforts and provide an overview of the scientific and practical difficulties inherent in managing various fMRI data-sharing approaches. Next, we detail the organization, design and foundation of the fMRIDC, ranging from its current capabilities to the issues involved in the submitting and requesting of data. We discuss how a publicly accessible database enables other fields to develop relevant tools that can aid in the growth of understanding of cognitive processes. Information retrieval and meta-analytic techniques can be used to search, sort and categorize study information with a view towards subjecting study data to secondary 'meta-' and 'mega-analyses'. In addition, we detail the technical and policy challenges that have had to be addressed in the formation of the Data Center. Among others, these include: human subject confidentiality issues; ensuring investigator's rights; heterogeneous data description and organization; development of search tools; and data transfer issues. We conclude with comments concerning the future of the fMRIDC effort, its role in promoting the sharing of neuroscientific data, and how this may alter the manner in which studies are published.

Brain↗

PubMed2003

ArrayTrack--supporting toxicogenomic research at the U.S. Food and Drug Administration National Center for Toxicological Research.

The mapping of the human genome and the determination of corresponding gene functions, pathways, and biological mechanisms are driving the emergence of the new research fields of toxicogenomics and systems toxicology. Many technological advances such as microarrays are enabling this paradigm shift that indicates an unprecedented advancement in the methods of understanding the expression of toxicity at the molecular level. At the National Center for Toxicological Research (NCTR) of the U.S. Food and Drug Administration, core facilities for genomic, proteomic, and metabonomic technologies have been established that use standardized experimental procedures to support centerwide toxicogenomic research. Collectively, these facilities are continuously producing an unprecedented volume of data. NCTR plans to develop a toxicoinformatics integrated system (TIS) for the purpose of fully integrating genomic, proteomic, and metabonomic data with the data in public repositories as well as conventional (Italic)in vitro(/Italic) and (Italic)in vivo(/Italic) toxicology data. The TIS will enable data curation in accordance with standard ontology and provide or interface a rich collection of tools for data analysis and knowledge mining. In this article the design, practical issues, and functions of the TIS are discussed through presenting its prototype version, ArrayTrack, for the management and analysis of DNA microarray data. ArrayTrack is logically constructed of three linked components: a) a library (LIB) that mirrors critical data in public databases; b) a database (MicroarrayDB) that stores microarray experiment information that is Minimal Information About a Microarray Experiment (MIAME) compliant; and c) tools (TOOL) that operate on experimental and public data for knowledge discovery. Using ArrayTrack, we can select an analysis method from the TOOL and apply the method to selected microarray data stored in the MicroarrayDB; the analysis results can be linked directly to gene information in the LIB.

Databases, Factual↗

PubMed2005

An information-driven approach to pharmacogenomics.

Effective information management of the pharmacogenomics discipline presents many unique challenges. Genetic and genomic data generated via high-throughput methods need to be integrated with phenotypic data which are defined at multiscale levels, ranging from the molecular to the clinical level. Repositories storing these data are distributed and vary in terms of syntax and semantics which result in issues concerning data exchange and integration. The application of the emerging semantic web offers a promising solution to these interoperability issues.

Animals↗

PubMed2005

Local coexpression domains of two to four genes in the genome of Arabidopsis.

Expression of genes in eukaryotic genomes is known to cluster, but cluster size is generally loosely defined and highly variable. We have here taken a very strict definition of cluster as sets of physically adjacent genes that are highly coexpressed and form so-called local coexpression domains. The Arabidopsis (Arabidopsis thaliana) genome was analyzed for the presence of such local coexpression domains to elucidate its functional characteristics. We used expression data sets that cover different experimental conditions, organs, tissues, and cells from the Massively Parallel Signature Sequencing repository and microarray data (Affymetrix) from a detailed root analysis. With these expression data, we identified 689 and 1,481 local coexpression domains, respectively, consisting of two to four genes with a pairwise Pearson's correlation coefficient larger than 0.7. This number is approximately 1- to 5-fold higher than the numbers expected by chance. A small (5%-10%) yet significant fraction of genes in the Arabidopsis genome is therefore organized into local coexpression domains. These local coexpression domains were distributed over the genome. Genes in such local domains were for the major part not categorized in the same functional category (GOslim). Neither tandemly duplicated genes nor shared promoter sequence nor gene distance explained the occurrence of coexpression of genes in such chromosomal domains. This indicates that other parameters in genes or gene positions are important to establish coexpression in local domains of Arabidopsis chromosomes.

Arabidopsis↗

PubMed1999

A six-year epidemiologic review of pneumonia in Department of Veterans Affairs facilities.

OBJECTIVE: Pneumonia and acute lower respiratory infections are a major problem in the United States and worldwide. As one of the largest health care organizations in the United States, the Department of Veterans Affairs is an ideal location for an epidemiologic review of pneumonia over an extended period of time. METHODS: Data for this study were retrieved from the Department of Veterans Affairs Austin Automation Center, the central repository for patient data in the Veterans Health Administration (VHA). In addition, specific data regarding penicillin-resistant Streptococcus pneumoniae in VHA facilities were obtained from an annual electronic nationwide census. RESULTS: The case rate of pneumonias as a discharge diagnosis increased during the 6-year period. For the diagnosis group of bronchopneumonia and pneumonia with organism unspecified, the largest subset examined, total numbers and rates for this specific diagnosis increased during the study period. When fiscal year (FY)91 and FY96 were compared, rates increased for three diagnoses: overall pneumonia, pneumonia in infectious diseases classified elsewhere, and pneumococcal pneumonia. Decreases in rates occurred between FY91 and FY96 for pneumonia caused by other specified organisms and other bacterial pneumonia. The total number of discharges from VHA facilities decreased during the 6-year period. CONCLUSIONS: The numbers of episodes of bronchopneumonia and pneumonia with organism unspecified, the largest pneumonia subset, increased during the 6-year period to greater than 27,000 cases. As the number of total discharges from the VHA decreased, the combination of increasing actual numbers and decreasing discharges yielded increased rates for overall pneumonia and certain subsets. These data should be useful in developing aggressive preventive strategies.

Aged↗

PubMed2004

The ArrayExpress gene expression database: a software engineering and implementation perspective.

MOTIVATION: The lack of microarray data management systems and databases is still one of the major problems faced by many life sciences laboratories. While developing the public repository for microarray data ArrayExpress we had to find novel solutions to many non-trivial software engineering problems. Our experience will be both relevant and useful for most bioinformaticians involved in developing information systems for a wide range of high-throughput technologies. RESULTS: ArrayExpress has been online since February 2002, growing exponentially to well over 10,000 hybridizations (as of September 2004). It has been demonstrated that our chosen design and implementation works for databases aimed at storage, access and sharing of high-throughput data. AVAILABILITY: The ArrayExpress database is available at http://www.ebi.ac.uk/arrayexpress/. The software is open source. CONTACT: ugis@ebi.ac.uk.

Algorithms↗

PubMed2004

Toxicogenomics in risk assessment: an overview of an HESI collaborative research program.

The value of genomic approaches in hypothesis generation is being realized as a tool for understanding toxicity and consequently contributing to an assessment of drug and chemical safety. In 1999 the membership of the International Life Sciences Institute Health and Environmental Sciences Institute formed a committee to develop a collaborative scientific program to address issues, challenges, and opportunities afforded by the emerging field of toxicogenomics. Experts and advisors from academia and government laboratories participate on the committee, along with approximately 30 corporate member organizations from the pharmaceutical, agrochemical, chemical, and consumer products industries. The committee has designed, conducted, and analyzed numerous toxicogenomic experiments within the broad fields of hepatotoxicity, nephrotoxicity, and genotoxicity. The considerable body of data generated by these programs has been instrumental in increasing understanding of sources of biological and technical variability in the alignment of toxicant-induced transcription changes with the accepted mechanism of action of these agents and the challenges in the consistent analysis and sharing of the voluminous data sets generated by these approaches. Recognizing the importance of standardized microarray data formats and public repository databases as the mechanism by which microarray data can be compared and interpreted by the scientific community, the committee has partnered with the European Bioinformatics Institute to develop a database to house the data generated by its collaborative research.

Gene Expression Profiling↗

PubMed2001

A formal method to resolve temporal mismatches in clinical databases.

Overcoming data heterogeneity is essential to the transfer of decision-support programs to legacy databases and to the integration of data in clinical repositories. Prior methods have focused primarily on problems of differences in terminology and patient identifiers, and have not addressed formally the problem of temporal data heterogeneity, even though time is a necessary element in storing, manipulating, and reasoning about clinical data. In this paper, we present a method to resolve temporal mismatches present in clinical databases. This method is based on a foundational model of time that can formalize various temporal representations. We use this temporal model to define a novel set of twelve operators that can map heterogeneous time-stamped data into a uniform temporal scheme. We present an algorithm that uses these mapping operators, and we discuss our implementation and evaluation of the method as a software program called Synchronus.

Algorithms↗

PubMed1996

Integrating data from natural language processing into a clinical information system.

Demographic data extracted from discharge summaries by natural language processing was compared to data gathered by a conventional hospital admitting system. Discrepancies in data were noted in names, age, sex, race, and ethnicity. Some differences are attributable to errors in collection: interaction with patient, dictation, transcription, and data entry. Very few differences were due to errors in natural language processing. Other differences can be used to critique existing data, or to enhance data with more detailed information. Discrepancies in data as elementary as patient demographics raise the issue of resolving conflicts when neither source of data is known to be more reliable. Clinical repositories can represent conflicting data from multiple sources, but clinical information systems must bear the cost of increased complexity in the application programs that will use the data.

Evaluation Studies as Topic↗

PubMed2004

Patient entry of information: evaluation of user interfaces.

BACKGROUND: Personal health records are web-based applications that allow patients to directly enter their own data into secure repositories in order to generate accessible profiles of medical information. OBJECTIVE: The authors evaluated a variety of user interfaces to determine whether different types of data entry methods employed by Personal health records may have an impact on the accuracy of patient-entered medical information. METHODS: Patients with disorders requiring treatment with thyroid hormone preparations were recruited to enter data into a web-based study application. The study application presented sequences of exercises that prompted free text entry, pick list selection, or radio button selection of information related to diagnoses, prescriptions, and laboratory test results. Entered data elements were compared to information abstracted from patients' clinic notes, prescription records, and laboratory test reports. RESULTS: Accuracy rates associated with the different data entry methods tested varied in relation to the complexity of requested information. Most of the data entry methods tested allowed for accurate entry of thyroid hormone preparation names, laboratory test names, and familiar diagnoses. Data entry methods that prompted guided abstraction of data elements from primary source documents were associated with more accurate entry of qualitative and quantitative information. CONCLUSIONS: Different types of data entry methods employed by Personal health records may have an impact on the accuracy of patient-entered medical information. Approaches that rely on guided entry of data elements abstracted from primary source documents may promote more accurate entry of information.

Drug Administration Schedule↗

PubMed1999

Design of a state-based workers' compensation information system.

BACKGROUND: California policy makers have long been hampered by a lack of credible information for use in making legislative or administrative changes to address serious problems in the workers' compensation system. In 1993, the legislature directed the California Division of Workers' Compensation to develop a Workers' Compensation Information System (WCIS). METHODS: An advisory committee developed key questions and identified data sources regarding injury/illness incidence, costs, promptness of benefit delivery, adequacy of benefits, satisfaction with services, and other outcomes. RESULTS: Key data elements were identified, mostly from existing mandated reporting forms for employers and physicians, and from standardized medical billings. Data collection will be carried out using: 1) rapid electronic data interchange (EDI) for a minimum number of data elements; 2) electronic collection of data on medical services for a sample of claims; and 3) surveys to address adequacy of benefits, satisfaction with services, return to work, and other outcomes. A state-based repository will analyze data and provide de-identified public use data files. CONCLUSIONS: The proposed WCIS will provide information to improve the performance and to increase the accountability of all the participants in California's Workers' Compensation system. More importantly, it will provide information which will allow improved quality in the provision of mandated benefits to injured workers.

Benchmarking↗

PubMed2006

Tomato Expression Database (TED): a suite of data presentation and analysis tools.

The Tomato Expression Database (TED) includes three integrated components. The Tomato Microarray Data Warehouse serves as a central repository for raw gene expression data derived from the public tomato cDNA microarray. In addition to expression data, TED stores experimental design and array information in compliance with the MIAME guidelines and provides web interfaces for researchers to retrieve data for their own analysis and use. The Tomato Microarray Expression Database contains normalized and processed microarray data for ten time points with nine pair-wise comparisons during fruit development and ripening in a normal tomato variety and nearly isogenic single gene mutants impacting fruit development and ripening. Finally, the Tomato Digital Expression Database contains raw and normalized digital expression (EST abundance) data derived from analysis of the complete public tomato EST collection containing >150,000 ESTs derived from 27 different non-normalized EST libraries. This last component also includes tools for the comparison of tomato and Arabidopsis digital expression data. A set of query interfaces and analysis, and visualization tools have been developed and incorporated into TED, which aid users in identifying and deciphering biologically important information from our datasets. TED can be accessed at http://ted.bti.cornell.edu.

Databases, Genetic↗

PubMed2005

Organelle DB: a cross-species database of protein localization and function.

To efficiently utilize the growing body of available protein localization data, we have developed Organelle DB, a web-accessible database cataloging more than 25,000 proteins from nearly 60 organelles, subcellular structures and protein complexes in 154 organisms spanning the eukaryotic kingdom. Organelle DB is the first on-line resource devoted to the identification and presentation of eukaryotic proteins localized to organelles and subcellular structures. As such, Organelle DB is a strong resource of data from the human proteome as well as from the major model organisms Saccharomyces cerevisiae, Arabidopsis thaliana, Drosophila melanogaster, Caenorhabditis elegans and Mus musculus. In particular, Organelle DB is a central repository of yeast data, incorporating results--and actual fluorescent imagesfrom ongoing large-scale studies of protein localization in S.cerevisiae. Each protein in Organelle DB is presented with its sequence and, as available, a detailed description of its function; functions were extracted from relevant model organism databases, and links to these databases are provided within Organelle DB. To facilitate data interoperability, we have annotated all protein localizations using vocabulary from the Gene Ontology consortium. We also welcome new data for inclusion in Organelle DB, which may be freely accessed at http://organelledb.lsi.umich.edu.

Animals↗